The healthcare sector is in the early stages of a revolution, involving the adoption of advanced robotics. Medical applications are reaching unimaginable levels of precision, cost and complexity, with an orderliness that begs for automation. Robots and collaborative robots (cobots) have proven pivotal in addressing challenges ranging from ballooning costs to performing complex surgeries, from aged care/rehab to cleaning/sterilization.
There is real value in an exploration of exactly how cobots and robots are becoming both commonplace and tailored to specific medical applications that enhance patient outcomes and operational efficiencies in healthcare.
The place of robots in healthcare is rapidly growing, as device costs fall and capabilities rise. These are not typically the humanoid simulants of the classic imagining, but non generalist, narrowly task-dedicated machines. Source: Maxk
The role of robotics in healthcare
The integration of automation into healthcare isn't new. For example, the AIDS epidemic drove the automation of virology testing in the 1980s, as blood testing rates exploded. Recent technology advances have greatly widened the scope of what is possible. Robots are now being designed to handle highly patient-contact and real-time medical tasks that demand precise motion, rigorous consistency in performance, and potentially high-risk physical interaction with patients and medics.
Medical robots can be broadly divided into three categories.
Medical robots are fully or partially autonomous systems that perform medical or general environmental/service tasks independently or with minimal human intervention.
Medical cobots are robots that are designed to work alongside humans, augmenting their capabilities while ensuring safety throughout their close interactions. These typically have less independence and are designed to respond to momentary instructions as subordinate assistants.
Waldos are a subcategory of cobots that typically get described as robots, but actually have little or no operational automation or capacity for independent action, but enhance direct operator capabilities to improve healthcare delivery.
Automation has revolutionized aspects of healthcare in small and large ways, but tailoring devices to specific applications requires consideration specialization to deliver task-specific precision, dexterity, patient/staff safety and non-disruptive and efficient integration into clinical workflows.
Cobots versus robots
When determining whether a robot or cobot is best suited for a medical or medical-related task, it's essential to understand the nature of the task and the environment in which the planned device or system will function.
Cobots are typically designed to assist human workers in repetitive, skill-substitution or physically demanding tasks. This can range from moving heavy medical equipment and assisting with patient lift or mobility, to performing routine tasks like disinfection, through to precision/skill tasks like taking routine blood samples or assisting with surgeries.
Medical cobots' fundamental utility lies in their collaboration with healthcare professionals in task-assistance and rack-substitution while ensuring staff and patient safety.
Cobots are typically equipped with force sensors and adaptive programming that aims to prevent them from causing harm to humans, allowing them to participate in rehabilitation, patient handling and clinical settings where frequent human-robot interaction is required. This can range from gophers (fetch/deliver automata) to tissue interaction machines performing limited-autonomy routines such as blood sample collection and post-surgery wound suturing.
Surgical devices like the Da Vinci system, for example, allow surgeons to perform minimally invasive surgeries with enhanced precision and control, but despite being called robots, they operate under direct human control, except in research (non-patient contact) contexts. They have very limited automated capability, but in all realistic use they are Waldo devices.
Autonomous robots, in contrast, possess full autonomy for tasks that require high precision and auditable and unsupervised (or lightly supervised) certainty, such as wound closure/dressing, drug dispensing, and laboratory automation.
They are typically developed to perform complex procedures with minimal (or no) human input, achieving accuracy and repeatability that surpasses human capabilities.
While both cobots and robots have a spectrum of distinct and overlapping functional advantages, choosing the right operational mode depends on the nature of the task, the level of human interaction/supervision and the value of the task outcomes.
Key areas where robots and cobots are transforming medicine
Surgical robotics
One of the most significant potential contributions of robotics in healthcare is in the incipient field of robotic surgery.
'Incipient' is a carefully chosen word. Citing realistic definitions of the leading 'surgical robot', the DaVinci equipment that is leader in the field is defined thus….
"The system mimics the surgeon's hand movements in real time. It cannot be programmed, nor can it make decisions on its own to move or perform any type of surgical maneuver. So, while the general term "robotic surgery" is often used to refer to our technology, it is not robotic surgery in the strictest sense of the term."
These surgical Waldo devices are increasingly being used for minimally invasive procedures, enabling surgeons to perform delicate tasks with enhanced precision.
These systems typically consist of a series of robotic arms controlled by a surgeon at a console, with ZERO AUTOMATION CAPABILITY. The effector arms are equipped with specialized tools and cameras that allow the surgeon to operate with increased dexterity and visibility.
These systems are referred to as robots despite this and are revolutionizing laparoscopic surgery by allowing surgeons to perform complex procedures through small incisions, leading to quicker recovery times and less postoperative pain for patients.
Tailoring robots for surgical applications involves much more development than has been successfully demonstrated thus far. Experimental autonomous systems have in fact been demonstrated in experimental settings, performing basic wound closure with moderate success - on animal cadavers!
The integrating of ultra-advanced imaging systems, haptic feedback for real-time tissue response assessment, and hugely powerful machine learning (ML) and AI to assist with decision-making during procedures is advancing.
Rehabilitation and physical therapy
In rehabilitation, cobots are already playing a vital role in assisting patients with recovery from injury or surgery. Assisting with repetitive physical therapy articulations/manipulations ensures consistency in motion and reduces the strain on therapists.
To illustrate, Ekso Bionics has developed exoskeletons that support the rehabilitation of patients who have suffered spinal cord injuries or strokes by sustaining or improving mobility, with huge long-term benefits. These wearable cobots provide patients with physical support and boosted strength to relearn basic movements and rebuild muscle. Other cobots, designed to assist in upper-body rehabilitation, help stroke patients regain motor function through repetitive, guided exercises.
The specific development processes of these cobots for rehabilitation required developers to consider factors such as patient safety, ease of use and adaptability to individual patients' needs. These generic needs were in addition to the programming, functional, motion range and strength requirements of the devices.
The ability of cobots to actively monitor patient progress and adjust the intensity of exercises in real-time is central to the provision of these services, without which their effectiveness in rehabilitation would be minimal. The ML/AI and sensory implications of this depth of understanding/sensitivity are significant.
Patient assistance and caregiving
The labor content in healthcare is in daily and direct patient interaction, in tasks such as lifting and transporting patients or assisting them in daily activities like feeding and bathing. Cobots for these tasks are designed to reduce the workload on nurses and caregivers, freeing more of their time to focus on the more complex and human-interactive aspects of patient care.
An example of this is RIBA, a robot developed in Japan that assists in lifting patients from beds to wheelchairs. Designed to have a cute, cartoon, toy-like and friendly demeanor, RIBA aims to induce a more comfortable impression with patients, while taking on the physical strain of caregiving.
Tailoring such patient-handling caregiving cobots involves creating a balance of great power with dexterity and active sensing to ensure they can handle the assigned tasks while remaining safe for interaction with vulnerable patients.
The integration of natural language processing (NLP) and AI equips these machines to communicate effectively with patients and healthcare staff, giving information and receiving instructions reliably.
Medical laboratory automation
Cobots and robots are at their most advanced and time served in transforming medical laboratories by automating repetitive tasks such as sample handling, testing and data analysis. Such tasks are traditionally performed by scientists and skilled analysts for reliability and discipline purposes. But the explosion in volume of testing subsequent to the AIDS epidemic made such labor insufficient. Automated systems were rapidly developed to process large volumes of samples quickly and accurately, reducing the risk of human error and speeding up diagnostic processes. This sector has remained active and fast growing in the subsequent decades.
To illustrate, Tecan's liquid handling robots are widely employed in clinical and pathology laboratories for tasks such as sample transfer, diluent addition and sample assessment, barcode reading and sterilization. These machines can handle thousands of samples in a single day, with high precision and consistency.
These robots are typically highly adaptable, able to switch between processes, vial diameters and evaluation tools for diverse tasks without requiring extensive reprogramming.
Gophers/scutters in medical settings
One of the more prevalent and lower effort applications for robots or cobots in a medical environment is in the automation of delivery and cleaning tasks.
The use of valuable and overstretched staff to fetch and carry basic equipment and consumables is easily and effectively substituted by surprisingly low cost and effective devices - not fundamentally different from waiter and room service robots. These devices have full autonomy while task-engaged. This involves route planning and exception/obstruction management and route planning, plus interfaces to security doors, elevators and even alarm systems.
Scutters are a similar class, involved in cleaning and sterilization tasks. Hospital cleanliness is key to medical outcomes, and robot cleaned/sterilized spaces are typically of a higher standard than human cleaned, at a lower cost.
Challenges in developing robots for medical applications
The potential benefits of robotics in healthcare are extensive and only lightly explored. There are, however, many barriers and challenges that scientists, developers and healthcare research funders must overcome in creating robots and cobots for increasingly delicate, precise and high-risk medical applications.
Regulatory compliance will be a huge hurdle. Certifying a self-driving car is essentially trivial in comparison with the hurdles a surgical robot must overcome. Medical robots and cobots must meet the most stringent regulatory environment of them all. The U.S. Food and Drug Administration or the European Medicines Agency are cautious and thorough, with processes that are time-consuming and expensive
Human-robot interaction is a challenge as human experience in robotic environments is limited. While cobots are designed to work alongside and interface to humans, people are not necessarily equipped for this. Ensuring that robots can understand and respond to subtle cues, voice commands and gestures but also tone of voice is essential for collaboration to be comfortable for the people.
The high CAPEX of more advanced medical robots is significant and a barrier to widespread adoption. While the benefits are clear, not all healthcare providers have the capacity to invest so heavily, so awaiting lower costs is common.
Effective staff training is key to low friction adoption. Knowing that healthcare professionals can operate robots and cobots safely and effectively us a bare minimum requirement. The investment in education and ongoing support is another financial challenge for organizations with limited resources.
The outlook — expanding applications, AI/ML integration
The future of robots and cobots in healthcare will have two main strands; rapid expansion of demand for devices and systems for the more mundane and menial tasks healthcare entails; and slower but highly pressured expansion of automation into sensitive, intricate and patient-engaged roles.
For instance, AI-driven automation already assists with remote and in-person diagnostics by analyzing patient data in real-time and suggesting treatment options to doctors. Gathering physiological diagnostic information is already feasible — automated visual, ultrasonic, auscultation and imaging tools are widely used and increasingly trusted and relied upon.
AI/ML already enhance the adaptability of cobots, allowing them to learn from human actions and improve their performance over time. The increase in processing capacity and learning algorithms that is already under way implies an exponential growth in this adaptive learning.
The future of healthcare robotics is one of immense promise. Devices and systems built to meet the specific demands of medical applications will improve patient outcomes, reduce costs, and streamline service delivery. The progressive advances in the software and hardware of robotics and AI/ML are driving toward robots that are smarter, more adaptable, and more capable than ever before.
This is not about replacing human effort but augmenting the human touch, allowing people to focus on the human patient, while the machine can focus on process, detail and the hard-slog of analytical cross referencing for diagnostics — or just cleaning, because that's just as important in many ways.